Navigating Public Opinion: Enhancing Sentiment Analysis on Social Media with CNN and SVM

Social networking sites have become essential forums for sharing thoughts, feelings, and criticism on a range of subjects. Sentiment analysis from user-generated information is essential for comprehending consumer behavior, market trends, and public opinion. In order to improve sentiment analysis across social media platforms, we provide a novel method in this research that integrates convolutional neural networks with support vector machines (SVM). First, we do trend analysis on a dataset of Twitter comments labeled favorable, adverse, and neutral to determine popular themes and attitudes. This trend analysis offers insightful information on how public opinion changes over time. Using CNNs, the suggested approach extracts relevant characteristics from the written information, identifying global as well as local patterns in the tweets. We can efficiently learn representations that capture the historical context and semantics associated with tweets by leveraging CNN's hierarchical structure. The SVM classifier further improves the sentiment forecasts based on the retrieved representations by using these learned features. Our model can perform robust sentiment analysis across a wide range of themes and linguistic styles found in social media data since it combines CNNs and SVMs. The Twitter dataset's experimental findings show how well our method works to reliably classify the sentiments that users convey in their remarks. Our CNN-SVM hybrid model performs better than other sentiment analysis techniques, attaining higher accuracy, precision, and recall scores. Furthermore, adding trend analysis to the sentiment analysis process improves its interpretability and provides deeper insights into the public's evolving attitudes and trending issues over social media platforms. All things considered, our suggested methodology offers a viable framework for analyzing sentiment across social networking sites, providing accuracy and interpretability by fusing CNN and SVM approaches with trend analysis. This study bears important implications for utilization in market studies, brand management, and public perception management in the digital era, as well as for the progress of sentiment analysis methodologies.